Method and system for performing analysis of social media messages

ABSTRACT

Disclosed is an improved method, system, and computer program product for analyzing social media content. Correlation analysis is used to analyze the social media data snippets. The correlation analysis is performed by reviewing other items of data that are outside of the message content itself. The present approach can advantageously be used to analyze and understand the content of social media message even where only very small quantities of data are provided within each message posting.

BACKGROUND

Given the widespread availability and usage of the internet byconsumers, many businesses have become interested in being able toeffectively monitor the content and commentary provided by suchconsumers. Interactive websites such as social networks and blogsprovide a wealth of useful information that can be advantageously usedby a business. It would be very desirable to allow the businesses tostay informed of actionable social networking content, for example, toidentify potential customers and possible sales leads or to identifyproblematic situations that may require immediate involvement ofcustomer service personnel.

With many forms of social media, the content of the social media messageis itself often sufficient to allow recognition of topic of thatcontent. This is because the social media content will often include alarge enough quantity of data to make it readily apparent what thatcontent is directed towards. For example, a blog posting will ofteninclude a large and detailed quantity of text and/or pictures that makethe topic of that blog posting very self-evident.

However, there are many types of social media content where it is verycommonplace to have very small quantities of content for each posting.For example, there are many types of systems that allow for sharingelectronic messages among a community of users, where the content ofeach message may only have a few words, phrases, or sentences. Twitteris a notable example of this type of message sharing system where eachmessage may only contain a very small snippet of text. Other examples ofmessage systems that may include very small message snippets includeInternet forums, electronic mailing lists, blogs and microblogs, andsocial networks. In any of these systems, users may post very briefmessages that can be read by other users of the system.

With these types of messages, it is very difficult by just looking atthe message itself to determine the topic of the message. This creates aproblem for any electronic system that seeks to perform automatedanalysis of the social media content.

Various techniques have been implemented in an attempt to address thisproblem. For example, hash tags are often used to provide the contextfor a particular message or tweet. An electronic analysis system can usethe hash tags to interpret the content or topic of the message, even ifthere is not a sufficient quantity of data in the message itself topermit this type of analysis.

However, this approach suffers from many drawbacks. First, thistechnique is useless if the user creating the message fails to use hashtags. Even when used, problems occur if the message creators useinconsistent hash tags, or if a mistake is made in the hash tag, e.g.,when a typographical or spelling error occurs in the hash tag.

Therefore, there is a need for an improved approach that can be used toanalyze any social media content, even social media content that containvery small quantities of data.

SUMMARY

Embodiments of the invention provide an improved method, system, andcomputer program product for analyzing social media content. Correlationanalysis is used to analyze the social media data snippets. Thecorrelation analysis is performed by reviewing other items of data thatare outside of the message content itself. The present approach canadvantageously be used to analyze and understand the content of socialmedia message even where only very small quantities of data are providedwithin each message posting.

Other additional objects, features, and advantages of the invention aredescribed in the detailed description, figures, and claims.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a system to implement social media analysis accordingto embodiments of the invention.

FIG. 2 illustrates a flowchart of an approach to perform social mediaanalysis according to embodiments of the invention.

FIG. 3 illustrates a flowchart of an approach to use social profile datato perform social media analysis according to embodiments of theinvention.

FIG. 4 illustrates a flowchart of an approach to use external messagesto perform social media analysis according to embodiments of theinvention.

FIG. 5 depicts a computerized system on which an embodiment of theinvention can be implemented.

DETAILED DESCRIPTION

The present disclosure is directed to an approach for analyzing socialmedia content. The present approach can be used to analyze andunderstand the content of social media message even where only verysmall quantities of data are provided within each message posting.

To accomplish this, correlation analysis is performed upon the socialmedia data. The correlation analysis is performed by reviewing otheritems of data that are outside of the message content itself.

Such other items of data may include, for example, external data aboutthe message sender. As just one example, a message may include only thewords “New galaxy sighted!” Semantic analysis may be performed uponother writings or messages made by the author in the past to identifythe semantic context of the current message. The analysis of the earlierwritings by the author may indicate, for example, that current messagesnippet is in the context of an astronomy message or alternatively inthe context of a new mobile telephone.

Correlation analysis may also be made by reviewing the content of othercontemporaneous messages or data. For example, consider a message thatonly includes the words “I just heard a loud boom!” This message doesnot by itself contain enough data to indicate specifically what it isdirected towards. In fact, even the author of the message may not knowwhat caused the loud noise and therefore is not capable of providing theexact context for the message within the body of the message content.However, analysis of other messages, news articles or data, combinedwith a review of the date, time, and location of the message author canprovide enough information to determine the context for the message. Forexample, identification of a news article or another message about a“gas pipe explosion” in the same geographic location as the messageauthor can be used to determine that the context of the message “I justheard a loud boom!” is referring to a message about a gas pipeexplosion.

This type of analysis may be used to correlate multiple message snippetsto deduce the context of the messages. For example, a first message maycontain the text “I heard a loud boom!” A second message may contain thetext “I smell gas!” A third message may contain the text “My stovestopped working.” By checking that all three messages are from the samegeneral geographic area and were posted at the same general timeframe,one can deduce that a gas pipe explosion has occurred and that all threemessages are directed to that same event.

FIG. 1 illustrates an example system 100 which may be employed in someembodiments of the invention to implement analysis of social mediamessage snippets. The system 100 includes one or more users at one ormore user stations 102 that operate the system 100. The user station 102comprises any type of computing station that may be used to operate orinterface with the applications in the system. Examples of such userstations include, for example, workstations, personal computers, orremote computing terminals. The user station 102 comprises a displaydevice, such as a display monitor, for displaying a user interface tousers at the user station. The user station also comprises one or moreinput devices for the user to provide operational control over theactivities of the system, such as a mouse or keyboard to manipulate apointing object in a graphical user interface to generate user inputs tothe enterprise application 104 and/or message analysis system 106.

A message analysis system 106 is used to analyze the social mediasnippets, which are received from one or more online social data sources108. Such social data sources include, for example, websites such as asocial network or blog or web feed (e.g., Facebook, Twitter, Blogger,and RSS). The content may include one or more comments (e.g., Facebookcomment, comment to a blog post, reply to a previous comment) oruploaded postings (e.g., images and associated metadata, text, richmedia, URLs) at one or more sources. The social data/content maytherefore comprise a variety of forms and/or types.

Correlation analysis is performed upon the social data. The correlationanalysis is performed by reviewing other items of data that are outsideof the message content itself. For example, the other items of externaldata may include social profile information about the message author.The correlation analysis may also be performed by reviewing the contentof other contemporaneous messages or data. This permits a context to bededuced for the message even where the author of the message does notand/or cannot provide exact context for the message within the messagebody.

According to some embodiments, integration is provided between themessage analysis system 106 and an enterprise application 104. Theenterprise application 104 comprises any business-related applicationthat provides visibility and control over various aspects of a business.Such enterprise/business applications can include, without limitation,customer relations management (“CRM”) applications, enterprise resourceplanning (“ERP”) applications, supply chain management applications, andother applications dealing with various finance, accounting,manufacturing, human resources, and/or distribution functions, to namebut a few examples. Exemplary enterprise application suites include,without limitation, Oracle Fusion Applications, Oracle eBusiness Suiteand JD Edwards Enterprise One, Oracle PeopleSoft applications, all ofwhich are available from Oracle Corporation of Redwood Shores, Calif.

For the purposes of explanation, one or more embodiments of theinvention are illustratively described with reference to CRMapplications. It is noted, however, that the invention may be applied toother types of enterprise applications as well, and is not to be limitedto CRM applications unless explicitly claimed as such.

The analysis results 110 are stored into a database in a computerreadable storage device 116. The computer readable storage devicecomprises any combination of hardware and software that allows for readyaccess to the data that is located at the computer readable storagedevice. For example, the computer readable storage device could beimplemented as computer memory operatively managed by an operatingsystem. The computer readable storage device could also be implementedas an electronic database system having storage on persistent and/ornon-persistent storage.

FIG. 2 shows a flowchart of an approach for implementing analysis ofsocial media snippets according to some embodiments of the invention. At202, data from social network systems are received into the system. Thesocial data may be either public social network messages or privatesocial network messages. Public social network messages include, forexample, publically available content from public blog sites, twittermessages, RSS data, and social media sites such as Facebook. Privatesocial network messages include, for example, content from internalcompany social networking sites. In some embodiments, the data that isreceived for processing includes non-social data. Such data includes,for example, enterprise data (e.g., email, chats, transcribed phoneconversations, transcribed videos).

Next, at 204, analysis is performed on external data that relate to themessage. This is not a direct analysis of the message content (e.g.,message text or message hashtag). Instead, this is an analysis of theother data associated with the message, such as message metadata orauthor information. The idea is that since the message content by itselfis too small and limited to allow accurate assessment of its content,contextual analysis can be performed to fill in the gaps.

At 206, contextual analysis is performed using the external data toidentify the context and/or topic of the social message snippet.Correlation is performed in this step to identify the context of themessage snippet. It is the combination of the analysis of the messagecontent together with the analysis of the external data related to themessage that permits accurate analysis of the message snippet, evenwhere the message snippet is lacking in message length and/or depth.

Appropriate actions are taken at 208 to respond to the analysis results.The analysis results should correspond to areas of analytical importancewith respect to the organizations that will be consuming the results ofthe system. For example, a business may seek to use the system toanalyze social network data to (1) identify sales leads and (2) identifycustomer relations issues and dissatisfied customers. If these are thebusiness'goals, then at least some of the analysis results will, in someembodiments, correspond to identification of the content that pertain tothese categories.

Different approaches can be taken to process the message snippets thatbecome actionable analysis results. Automated processing can beperformed using rules and workflow engine, where a set of rules isprovided in a rulebase. The rules identify how the analysis resultsshould be handled and directed within a business organization. Anotherpossible approach is to employ manual processing such that a userreviews the actionable social messages and manually takes action todirect the message to the appropriate destination.

Thereafter, the appropriate action is taken with respect to the message.For example, tickets can be sent to a social customer service cloudproduct, the identity of possible employment candidates can be sent toan HR department, opportunities can be provided to a CRM system, andproduct data/comments can be provided to ecommerce products and groups.

As an example, consider the scenario where message snippets such as“Internet down”, “web sites not coming up”, “my phone cut off” can beanalyzed and identified as a possible service issue for a localtelephone/DSL provider. The analysis may also confirm the geographicscope of the problem, e.g., based upon geographic locations of themessage snippets. In this situation, even without waiting for numerouscustomer service calls to be placed to the phone/DSL provider, thecompany can immediately identify and address the potential issues,thereby reducing customer downtime and perhaps correct the problembefore many/most customers even become aware that there may be an issue.

FIG. 3 shows a flowchart of one approach to implement analysis ofmessage snippets using external data. At 302, social profile data isacquired for the author of the message snippet. The social profile datamay include demographic information, including information about theperson's income, age, profession, and geographic location. The socialprofile data may also include psychographic information about themessage author. In addition, the social profile may include copies ofother messages and postings by the author. The data can be obtained fromany type of source, including social media and non-social media sources.

At 304, the social profile data is analyzed to identify informationabout the message author, including interests, concerns, affinity,and/or other items of interests about the author. In some embodiments,this analysis is performed in real-time for each author of interest. Inalternate embodiments, this type of analysis is generally performedahead of time and/or on an ongoing basis for authors of social mediacontent. By performing this type of analysis ahead of time, the messagesnippets can be analyzed more expeditiously and with more immediacy.

At 306, correlation analysis is performed to identify the context and/ortopic of the social message snippet. The content of the social messagesnippet is semantically analyzed in light of the social profile data toperform this type of analysis.

For example, latent semantic analysis (LSA), an advanced form ofstatistical language modeling, can be used to perform semantic analysisupon both the social profile data and the message snippet. This permitsthe system to understand the contextual and semantic significance ofterms that appear within the data. For example, by gathering data aboutthe author from his social profile, semantic analysis can be used tounderstand the difference between the term “Galaxy” used by the authorfor an astronomy context, “Galaxy” used to refer to the mobile phonebrand, and “Galaxy” the name of a professional soccer team.

FIG. 4 shows a flowchart of one approach to implement analysis ofmessage snippets using additional messages. At 402, additional externalmessages are received for analysis in conjunction with the messagesnippet. These additional external messages comprise any externalinformation including, for example, other message snippets, newsarticles, blogs, and forum postings.

At 404, location correlation is performed upon the messages. Locationcorrelation is performed to identify other messages that pertain to ororiginate from the same general geographic region as the message snippetis being analyzed. The location information may be obtained from themessage content themselves. The location may also be obtained from themessage origination location, e.g., Tweet location. The author locationmay also be used to infer a location or the message, e.g., from theauthor's demographic or social profile data. Filtering can be applied toremove any messages that are geographically distant from the messagesnippet.

At 406, timeframe correlation may be performed upon the multiplemessages. Timeframe correlation is performed to identify other messagesthat are relatively close in time to the subject of the message snippet.

Volume correlation is performed at 408. This step performs an analysisof message volume to determine, for example, whether there is a spike insocial media messaging at around the same time as the social messagesnippet being analyzed.

Using the results of the above correlations, analysis is performed at410 to identify the other messages that likely pertain to or correspondin some way with the message snippet. For example, messages thatoriginate at the same point in time, from the same geographic regions,and/or at the same moment as a spike in messaging is likely to berelevant to the message snippet.

Semantic analysis and classifications are also performed upon themessages, to classify and filter the messages. The classificationanalysis permits the system to create and apply filters to identifythemes, and to cluster together like-minded messages, topics,conversations, and content. This allows the system to deduce the contextof the message snippet from the other related messages.

Semantic filtering may also be applied to the analysis. Semanticfiltering is a mechanism that is provided to minimizemiss-categorizations of the social data. Semantic filtering is used toremove the irrelevant material from the social data to reduce theoccurrence of false positives, false negatives, and inappropriateresponses/rejections within the actionable data. This permits theresulting data to be more relevant and accurate when provided to theenterprise applications. In some embodiments, all social data content issubject to semantic filtering to reduce the excess “noise” of irrelevantdata.

Therefore, what has been described is an improved approach forimplementing a system, method, and computer program product to analyzesocial media messages. Correlation analysis is used to analyze thesocial media data snippets. The correlation analysis is performed byreviewing other items of data that are outside of the message contentitself. The present approach can advantageously be used to analyze andunderstand the content of social media message even where only verysmall quantities of data are provided within each message posting.

System Architecture Overview

FIG. 5 is a block diagram of an illustrative computing system 1400suitable for implementing an embodiment of the present invention.Computer system 1400 includes a bus 1406 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 1407, system memory 1408 (e.g., RAM),static storage device 1409 (e.g., ROM), disk drive 1410 (e.g., magneticor optical), communication interface 1414 (e.g., modem or Ethernetcard), display 1411 (e.g., CRT or LCD), input device 1412 (e.g.,keyboard), data interface 1433 is connected to database 1432 at location1431, and cursor control.

According to one embodiment of the invention, computer system 1400performs specific operations by processor 1407 executing one or moresequences of one or more instructions contained in system memory 1408.Such instructions may be read into system memory 1408 from anothercomputer readable/usable medium, such as static storage device 1409 ordisk drive 1410. In alternative embodiments, hard-wired circuitry may beused in place of or in combination with software instructions toimplement the invention. Thus, embodiments of the invention are notlimited to any specific combination of hardware circuitry and/orsoftware. In one embodiment, the term “logic” shall mean any combinationof software or hardware that is used to implement all or part of theinvention.

The term “computer readable medium” or “computer usable medium” as usedherein refers to any medium that participates in providing instructionsto processor 1407 for execution. Such a medium may take many forms,including but not limited to, non-volatile media and volatile media.Non-volatile media includes, for example, optical or magnetic disks,such as disk drive 1410. Volatile media includes dynamic memory, such assystem memory 1408.

Common forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can read.

In an embodiment of the invention, execution of the sequences ofinstructions to practice the invention is performed by a single computersystem 1400. According to other embodiments of the invention, two ormore computer systems 1400 coupled by communication link 1415 (e.g.,LAN, PTSN, or wireless network) may perform the sequence of instructionsrequired to practice the invention in coordination with one another.

Computer system 1400 may transmit and receive messages, data, andinstructions, including program, i.e., application code, throughcommunication link 1415 and communication interface 1414. Receivedprogram code may be executed by processor 1407 as it is received, and/orstored in disk drive 1410, or other non-volatile storage for laterexecution.

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Forexample, the above-described process flows are described with referenceto a particular ordering of process actions. However, the ordering ofmany of the described process actions may be changed without affectingthe scope or operation of the invention. The specification and drawingsare, accordingly, to be regarded in an illustrative rather thanrestrictive sense.

What is claimed is:
 1. A computer implemented method for correlatingdisparate social media data and identifying a context for correlatedmedia data, the method comprising: analyzing social media messages usingan analysis unit, the analysis unit being communicatively coupled toreceive inputs from one or more online social data sources and toprovide analysis outputs to a workflow engine, the analysis unitcorrelating the social media messages to identify a context based atleast upon analysis of message contents, metadata, and social profiledata, the analysis of the social media messages performed by a processcomprising: receiving social media content from the one or more onlinesocial data sources transmitted over one or more electroniccommunications links, the social media content comprising at leastmessage contents, metadata, and social profile data, determining ageographic location of the message contents, removing the messagecontents that are beyond a geographical distance from the geographiclocation of at least one of the message contents, performing latentsemantic analysis on remaining message contents using the metadata andthe social profile data to determine semantic significance of theremaining message contents, clustering the messages into one or moreclusters by classifying the messages based on at least the results ofperforming latent semantic analysis, and correlating the at least one ofthe one or more clusters to identify a context for the remaining messagecontents using at least the metadata and the remaining message contentsto generate correlation results, processing the correlation resultsusing the workflow engine, the workflow engine processing thecorrelation results using a set of rules provided in a rulebase, the setof rules identifying how actionable items should be handled or directedwithin an organization, the correlation results processed by: receivingthe correlation results from the analysis unit transmitted over one ormore electronic communications links, identifying one or more actionableitems based at least in part on the received correlation results,automatically processing at least some of the one or more actionableitems using the workflow engine and the set of rules.
 2. The method ofclaim 1, wherein the social profile data comprises demographic orpsychographic information about an author of the social media content.3. The method of claim 1, wherein the social profile data ispre-analyzed to establish analysis data pertaining to an author prior tocreation of the social media content.
 4. The method of claim 1, whereincorrelating comprises using at least one of location data, time data,volume data, and subject matter data.
 5. The method of claim 1, in whichsemantic filtering is performed to reduce occurrence of false positives,false negatives or inappropriate responses.
 6. The method of claim 1,wherein the analysis results are obtained that create actionable itemsfor an enterprise software system and the enterprise software systemcomprises at least one of a customer relations management application,an enterprise resource planning applications, and a supply chainmanagement application.
 7. The method of claim 1, further comprisingfiltering to reduce occurrences of false positives, false negatives orinappropriate responses, the filtering comprising at least semanticfiltering; storing the correlation results in an electronic database onone or more persistent computer readable storage devices; whereincorrelating comprises using at least one of location data, time data,volume data, and subject matter data; wherein the workflow engine ispart of an enterprise software system comprising at least one of acustomer relations management application, enterprise resource planningapplications, and a supply chain management application, the actionableitems comprising at least one of identification of sales leads,identification of customer relations issues, or identification ofdissatisfied customers; wherein the social media content comprisesmessages from a messaging system that are distributed based uponidentification of followers for the messages, the social media contentoriginating from at least one of public social networks or privatesocial media networks, the public social media networks including atleast blog sites, forums, twitter messages, RSS data, and Facebook, andthe private social networks including at least internal company socialnetworking sites, emails, chats, transcribed phone conversations, andtranscribed videos; wherein the social profile data comprises at leastpsychographic information about an authors income, age, profession,geographic location, concerns, affinities, or other areas of interest,and wherein the social profile data is pre-analyzed to establishanalysis data pertaining to the author prior to creation of the socialmedia content; and wherein the geographic location is inferred fromauthor location data or social profile data.
 8. A non-transitorycomputer readable medium having stored thereon a sequence ofinstructions which, when executed by a processor causes the processor toexecute a method for analyzing social media data correlating disparatesocial media data and identifying a context for correlated media data,the method comprising: analyzing social media messages using an analysisunit, the analysis unit being communicatively coupled to receive inputsfrom one or more online social data sources and to provide analysisoutputs to a workflow engine, the analysis unit correlating the socialmedia messages to identify a context based at least upon analysis ofmessage contents, metadata, and social profile data, the analysis of thesocial media messages performed by a process comprising: receivingsocial media content from the one or more online social data sourcestransmitted over one or more electronic communications links, the socialmedia content comprising at least message contents, metadata, and socialprofile data, determining a geographic location of the message contents,removing the message contents that are beyond a geographical distancefrom the geographic location of at least one of the message contents,performing latent semantic analysis on remaining message contents usingthe metadata and the social profile data to determine semanticsignificance of the remaining message contents, clustering the messagesinto one or more clusters by classifying the messages based on at leastthe results of performing latent semantic analysis, and correlating theat least one of the one or more clusters to identify a context for theremaining message contents using at least the metadata and the remainingmessage contents to generate correlation results, processing thecorrelation results using the workflow engine, the workflow engineprocessing the correlation results using a set of rules provided in arulebase, the set of rules identifying how actionable items should behandled or directed within an organization, the correlation resultsprocessed by: receiving the correlation results from the analysis unittransmitted over one or more electronic communications links,identifying one or more actionable items based at least in part on thereceived correlation results, automatically processing at least some ofthe one or more actionable items using the workflow engine and the setof rules.
 9. The computer readable medium of claim 8, wherein the socialprofile data comprises demographic or psychographic information about anauthor of the social media content.
 10. The computer readable medium ofclaim 8, wherein the social profile data is pre-analyzed to establishanalysis data pertaining to an author prior to creation of the socialmedia content.
 11. The computer readable medium of claim 8, whereincorrelating comprises using at least one of location data, time data,volume data, and subject matter data.
 12. The computer readable mediumof claim 8 in which semantic filtering is performed to reduce occurrenceof false positives, false negatives or inappropriate responses.
 13. Thecomputer readable medium of claim 8, wherein the analysis results areobtained that create actionable items for an enterprise software systemand the enterprise software system comprises at least one of a customerrelations management application, an enterprise resource planningapplications, and a supply chain management application.
 14. Thecomputer readable medium of claim 8, the method further comprisingfiltering to reduce occurrences of false positives, false negatives orinappropriate responses, the filtering comprising at least semanticfiltering; storing the correlation results in an electronic database onone or more persistent computer readable storage devices; whereincorrelating comprises using at least one of location data, time data,volume data, and subject matter data; wherein the workflow engine ispart of an enterprise software system comprising at least one of acustomer relations management application, enterprise resource planningapplications, and a supply chain management application, the actionableitems comprising at least one of identification of sales leads,identification of customer relations issues, or identification ofdissatisfied customers; wherein the social media content comprisesmessages from a messaging system that are distributed based uponidentification of followers for the messages, the social media contentoriginating from at least one of public social networks or privatesocial media networks, the public social media networks including atleast blog sites, forums, twitter messages, RSS data, and Facebook, andthe private social networks including at least internal company socialnetworking sites, emails, chats, transcribed phone conversations, andtranscribed videos; wherein the social profile data comprises at leastpsychographic information about an authors income, age, profession,geographic location, concerns, affinities, or other areas of interest,and wherein the social profile data is pre-analyzed to establishanalysis data pertaining to the author prior to creation of the socialmedia content; and wherein the geographic location is inferred fromauthor location data or social profile data.
 15. A computer system foranalyzing social media data, comprising: a computer processor to executea set of program code instructions; and a memory to hold the programcode instructions, in which the program code instructions comprisesprogram code to perform acts comprising: analyzing social media messagesusing an analysis unit, the analysis unit being communicatively coupledto receive inputs from one or more online social data sources and toprovide analysis outputs to a workflow engine, the analysis unitcorrelating the social media messages to identify a context based atleast upon analysis of message contents, metadata, and social profiledata, the analysis of the social media messages performed by a processcomprising: receiving social media content from the one or more onlinesocial data sources transmitted over one or more electroniccommunications links, the social media content comprising at leastmessage contents, metadata, and social profile data, determining ageographic location of the message contents, removing the messagecontents that are beyond a geographical distance from the geographiclocation of at least one of the message contents, performing latentsemantic analysis on remaining message contents using the metadata andthe social profile data to determine semantic significance of theremaining message contents, clustering the messages into one or moreclusters by classifying the messages based on at least the results ofperforming latent semantic analysis, and correlating the at least one ofthe one or more clusters to identify a context for the remaining messagecontents using at least the metadata and the remaining message contentsto generate correlation results, processing the correlation resultsusing the workflow engine, the workflow engine processing thecorrelation results using a set of rules provided in a rulebase, the setof rules identifying how actionable items should be handled or directedwithin an organization, the correlation results processed by: receivingthe correlation results from the analysis unit transmitted over one ormore electronic communications links, identifying one or more actionableitems based at least in part on the received correlation results,automatically processing at least some of the one or more actionableitems using the workflow engine and the set of rules.
 16. The system ofclaim 15, wherein the social profile data is pre-analyzed to establishanalysis data pertaining to an author prior to creation of the socialmedia content.
 17. The system of claim 15, wherein correlating comprisesusing at least one of location data, time data, volume data, and subjectmatter data.
 18. The system of claim 15 in which semantic filtering isperformed to reduce occurrence of false positives, false negatives orinappropriate responses.
 19. The system of claim 15, wherein theanalysis results are obtained that create actionable items for anenterprise software system and the enterprise software system comprisesat least one of a customer relations management application, anenterprise resource planning applications, and a supply chain managementapplication.
 20. The system of claim 15, the actions further comprisingfiltering to reduce occurrences of false positives, false negatives orinappropriate responses, the filtering comprising at least semanticfiltering; storing the correlation results in an electronic database onone or more persistent computer readable storage devices; whereincorrelating comprises using at least one of location data, time data,volume data, and subject matter data; wherein the workflow engine ispart of an enterprise software system comprising at least one of acustomer relations management application, enterprise resource planningapplications, and a supply chain management application, the actionableitems comprising at least one of identification of sales leads,identification of customer relations issues, or identification ofdissatisfied customers; wherein the social media content comprisesmessages from a messaging system that are distributed based uponidentification of followers for the messages, the social media contentoriginating from at least one of public social networks or privatesocial media networks, the public social media networks including atleast blog sites, forums, twitter messages, RSS data, and Facebook, andthe private social networks including at least internal company socialnetworking sites, emails, chats, transcribed phone conversations, andtranscribed videos; wherein the social profile data comprises at leastpsychographic information about an authors income, age, profession,geographic location, concerns, affinities, or other areas of interest,and wherein the social profile data is pre-analyzed to establishanalysis data pertaining to the author prior to creation of the socialmedia content; and wherein the geographic location is inferred fromauthor location data or social profile data.